MétaCan
Menu
Back to cohort
Record W2172282087 · doi:10.1109/tmtt.2011.2163802

2-D Digital Predistortion (2-D-DPD) Architecture for Concurrent Dual-Band Transmitters

2011· article· en· W2172282087 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPredistortionAdjacent channel power ratioLinearizationElectronic engineeringMulti-band deviceComputer scienceAmplifierWidebandOrthogonal frequency-division multiplexingTransmitterLinearizerLinearityElectrical engineeringCMOSEngineeringChannel (broadcasting)TelecommunicationsPhysics

Abstract

fetched live from OpenAlex

This paper presents a novel 2-D digital-predistortion (2-D-DPD) technique that is applicable for linearization of concurrent dual-band transmitters. This technique uses a unique way for distortion compensation and linearization of dual-band transmitters by selecting, characterizing, and applying predistortion in each band separately. Compared to conventional linearization techniques, this 2-D-DPD method requires a lower sampling rate for digital-to-analog and analog-to-digital converters. The performance of the 2-D-DPD topology is evaluated using two modulated signals, Worldwide Interoperability for Microwave Access and wideband code-division multiple-access, separated in frequency by 100 MHz. The measurement results show an adjacent channel power ratio of less than -50 dBc and a normalized mean square error of less than -40 dB.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.218
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it